class_docstr: Class for masking of Niimg-like objects.
  
  MultiNiftiMapsMasker is useful when data from overlapping volumes
  and from different subjects should be extracted (contrary to
  :class:`nilearn.maskers.NiftiMapsMasker`).
  
  .. note::
      Inf or NaN present in the given input images are automatically
      put to zero rather than considered as missing data.
  
  Parameters
  ----------
  maps_img : 4D niimg-like object
      See :ref:`extracting_data`.
      Set of continuous maps. One representative time course per map is
      extracted using least square regression.
  
  mask_img : 3D niimg-like object, optional
      See :ref:`extracting_data`.
      Mask to apply to regions before extracting signals.
  
  allow_overlap : :obj:`bool`, default=True
      If False, an error is raised if the maps overlaps (ie at least two
      maps have a non-zero value for the same voxel).
  %(smoothing_fwhm)s
  %(standardize_maskers)s
  %(standardize_confounds)s
  high_variance_confounds : :obj:`bool`, default=False
      If True, high variance confounds are computed on provided image with
      :func:`nilearn.image.high_variance_confounds` and default parameters
      and regressed out.
  %(detrend)s
  %(low_pass)s
  %(high_pass)s
  %(t_r)s
  dtype : {dtype, "auto"}, optional
      Data type toward which the data should be converted. If "auto", the
      data will be converted to int32 if dtype is discrete and float32 if it
      is continuous.
  
  resampling_target : {"data", "mask", "maps", None}, default="data"
      Gives which image gives the final shape/size:
  
          - "data" means the atlas is resampled to the shape of the data if
            needed
          - "mask" means the maps_img and images provided to fit() are
            resampled to the shape and affine of mask_img
          - "maps" means the mask_img and images provided to fit() are
            resampled to the shape and affine of maps_img
          - None means no resampling: if shapes and affines do not match,
            a ValueError is raised.
  
  
  %(memory)s
  %(memory_level)s
  %(n_jobs)s
  %(verbose0)s
  reports : :obj:`bool`, default=True
      If set to True, data is saved in order to produce a report.
  %(masker_kwargs)s
  
  Attributes
  ----------
  maps_img_ : :obj:`nibabel.nifti1.Nifti1Image`
      The maps mask of the data.
  
  n_elements_ : :obj:`int`
      The number of overlapping maps in the mask.
      This is equivalent to the number of volumes in the mask image.
  
      .. versionadded:: 0.9.2
  
  Notes
  -----
  If resampling_target is set to "maps", every 3D image processed by
  transform() will be resampled to the shape of maps_img. It may lead to a
  very large memory consumption if the voxel number in maps_img is large.
  
  See Also
  --------
  nilearn.maskers.NiftiMasker
  nilearn.maskers.NiftiLabelsMasker
  nilearn.maskers.NiftiMapsMasker
code: def __init__(
      self,
      maps_img,
      mask_img=None,
      allow_overlap=True,
      smoothing_fwhm=None,
      standardize=False,
      standardize_confounds=True,
      high_variance_confounds=False,
      detrend=False,
      low_pass=None,
      high_pass=None,
      t_r=None,
      dtype=None,
      resampling_target="data",
      memory=Memory(location=None, verbose=0),
      memory_level=0,
      verbose=0,
      reports=True,
      n_jobs=1,
      **kwargs,
  ):
      self.n_jobs = n_jobs
      super().__init__(
          maps_img,
          mask_img=mask_img,
          allow_overlap=allow_overlap,
          smoothing_fwhm=smoothing_fwhm,
          standardize=standardize,
          standardize_confounds=standardize_confounds,
          high_variance_confounds=high_variance_confounds,
          detrend=detrend,
          low_pass=low_pass,
          high_pass=high_pass,
          t_r=t_r,
          dtype=dtype,
          resampling_target=resampling_target,
          memory=memory,
          memory_level=memory_level,
          verbose=verbose,
          reports=reports,
          **kwargs,
      )
  
file_path: nilearn/nilearn/maskers/multi_nifti_maps_masker.py
imports: import itertools
  from joblib import Memory, Parallel, delayed
  from .._utils import fill_doc
  from .._utils.niimg_conversions import iter_check_niimg
  from .nifti_maps_masker import NiftiMapsMasker
label_desc: Use image node_id 1 to create a new MultiNiftiMapsMasker object from inherited base classes: NiftiMapsMasker with example: obj = MultiNiftiMapsMasker(maps_img, mask_img, allow_overlap, smoothing_fwhm, standardize, standardize_confounds, high_variance_confounds, detrend, low_pass, high_pass, t_r, dtype, resampling_target, memory, memory_level, verbose, reports, n_jobs)